Gradient Dissent: Conversations on AI

Since reinforcement learning requires hefty compute resources, it can be tough to keep up without a serious budget of your own. Find out how the team at Facebook AI Research (FAIR) is looking to increase access and level the playing field with the help of NetHack, an archaic rogue-like video game from the late 80s.

Links discussed:

The NetHack Learning Environment:

https://ai.facebook.com/blog/nethack-learning-environment-to-advance-deep-reinforcement-learning/

Reinforcement learning, intrinsic motivation:

https://arxiv.org/abs/2002.12292

Knowledge transfer:

https://arxiv.org/abs/1910.08210

Tim Rocktäschel is a Research Scientist at Facebook AI Research (FAIR) London and a Lecturer in the Department of Computer Science at University College London (UCL). At UCL, he is a member of the UCL Centre for Artificial Intelligence and the UCL Natural Language Processing group. Prior to that, he was a Postdoctoral Researcher in the Whiteson Research Lab, a Stipendiary Lecturer in Computer Science at Hertford College, and a Junior Research Fellow in Computer Science at Jesus College, at the University of Oxford.

https://twitter.com/_rockt

Heinrich Kuttler is an AI and machine learning researcher at Facebook AI Research (FAIR) and before that was a research engineer and team lead at DeepMind.

https://twitter.com/HeinrichKuttler

https://www.linkedin.com/in/heinrich-kuttler/

Topics covered:

0:00 a lack of reproducibility in RL

1:05 What is NetHack and how did the idea come to be?

5:46 RL in Go vs NetHack

11:04 performance of vanilla agents, what do you optimize for

18:36 transferring domain knowledge, source diving

22:27 human vs machines intrinsic learning

28:19 ICLR paper - exploration and RL strategies

35:48 the future of reinforcement learning

43:18 going from supervised to reinforcement learning

45:07 reproducibility in RL

50:05 most underrated aspect of ML, biggest challenges?

Get our podcast on these other platforms:

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

YouTube: http://wandb.me/youtube

Soundcloud: http://wandb.me/soundcloud

Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices:

https://wandb.ai/gallery

From teaching at Stanford to co-founding Coursera, insitro, and Engageli, Daphne Koller reflects on the importance of education, giving back, and cross-functional research.

Daphne Koller is the founder and CEO of insitro, a company using machine learning to rethink drug discovery and development. She is a MacArthur Fellowship recipient, member of the National Academy of Engineering, member of the American Academy of Arts and Science, and has been a Professor in the Department of Computer Science at Stanford University. In 2012, Daphne co-founded Coursera, one of the world's largest online education platforms. She is also a co-founder of Engageli, a digital platform designed to optimize student success.

https://www.insitro.com/

https://www.insitro.com/jobs

https://www.engageli.com/

https://www.coursera.org/

Follow Daphne on Twitter: https://twitter.com/DaphneKoller

https://www.linkedin.com/in/daphne-koller-4053a820/

Topics covered:

0:00​ Giving back and intro

2:10​ insitro's mission statement and Eroom's Law

3:21​ The drug discovery process and how ML helps

10:05​ Protein folding

15:48​ From 2004 to now, what's changed?

22:09​ On the availability of biology and vision datasets

26:17​ Cross-functional collaboration at insitro

28:18​ On teaching and founding Coursera

31:56​ The origins of Engageli

36:38​ Probabilistic graphic models

39:33​ Most underrated topic in ML

43:43​ Biggest day-to-day challenges

Get our podcast on these other platforms:

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

YouTube: http://wandb.me/youtube

Soundcloud: http://wandb.me/soundcloud

Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices:

https://wandb.ai/gallery

Piero shares the story of how Ludwig was created, as well as the ins and outs of how Ludwig works and the future of machine learning with no code.

Piero is a Staff Research Scientist in the Hazy Research group at Stanford University. He is a former founding member of Uber AI, where he created Ludwig, worked on applied projects (COTA, Graph Learning for Uber Eats, Uber’s Dialogue System), and published research on NLP, Dialogue, Visualization, Graph Learning, Reinforcement Learning, and Computer Vision.

Topics covered:

0:00 Sneak peek and intro

1:24 What is Ludwig, at a high level?

4:42 What is Ludwig doing under the hood?

7:11 No-code machine learning and data types

14:15 How Ludwig started

17:33 Model performance and underlying architecture

21:52 On Python in ML

24:44 Defaults and W&B integration

28:26 Perspective on NLP after 10 years in the field

31:49 Most underrated aspect of ML

33:30 Hardest part of deploying ML models in the real world

Learn more about Ludwig: https://ludwig-ai.github.io/ludwig-docs/

Piero's Twitter: https://twitter.com/w4nderlus7

Follow Piero on Linkedin: https://www.linkedin.com/in/pieromolino/?locale=en_US

Get our podcast on these other platforms:

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

YouTube: http://wandb.me/youtube

Soundcloud: http://wandb.me/soundcloud

Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices:

https://wandb.ai/gallery

How Rosanne is working to democratize AI research and improve diversity and fairness in the field through starting a non-profit after being a founding member of Uber AI Labs, doing lots of amazing research, and publishing papers at top conferences.

Rosanne is a machine learning researcher, and co-founder of ML Collective, a nonprofit organization for open collaboration and mentorship. Before that, she was a founding member of Uber AI. She has published research at NeurIPS, ICLR, ICML, Science, and other top venues. While at school she used neural networks to help discover novel materials and to optimize fuel efficiency in hybrid vehicles.

ML Collective: http://mlcollective.org/

Controlling Text Generation with Plug and Play Language Models: https://eng.uber.com/pplm/

LCA: Loss Change Allocation for Neural Network Training: https://eng.uber.com/research/lca-loss-change-allocation-for-neural-network-training/

Topics covered

0:00 Sneak peek, Intro

1:53 The origin of ML Collective

5:31 Why a non-profit and who is MLC for?

14:30 LCA, Loss Change Allocation

18:20 Running an org, research vs admin work

20:10 Advice for people trying to get published

24:15 on reading papers and Intrinsic Dimension paper

36:25 NeurIPS - Open Collaboration

40:20 What is your reward function?

44:44 Underrated aspect of ML

47:22 How to get involved with MLC

Get our podcast on these other platforms:

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

YouTube: http://wandb.me/youtube

Tune in to our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices:

https://wandb.ai/gallery

In this episode of Gradient Dissent, Primer CEO Sean Gourley and Lukas Biewald sit down to talk about NLP, working with vast amounts of information, and how crucially it relates to national defense. They also chat about their experience of being second-time founders coming from a data science background and how it affects the way they run their companies. We hope you enjoy this episode!

Sean Gourley is the founder and CEO Primer, a natural language processing startup in San Francisco. Previously, he was CTO of Quid an augmented intelligence company that he cofounded back in 2009. And prior to that, he worked on self-repairing nano circuits at NASA Ames. Sean has a PhD in physics from Oxford, where his research as a road scholar focused on graph theory, complex systems, and the mathematical patterns underlying modern war.

Follow Sean on Twitter:

https://primer.ai/

https://twitter.com/sgourley

Topics Covered:

0:00 Sneak peek, intro

1:42 Primer's mission and purpose

4:29 The Diamond Age – How do we train machines to observe the world and help us understand it

7:44 a self-writing Wikipedia

9:30 second-time founder

11:26 being a founder as a data scientist

15:44 commercializing algorithms

17:54 Is GPT-3 worth the hype? The mind-blowing scale of transformers

23:00 AI Safety, military/defense

29:20 disinformation, does ML play a role?

34:55 Establishing ground truth and informational provenance

39:10 COVID misinformation, Masks, division

44:07 most underrated aspect of ML

45:09 biggest bottlenecks in ML?

Visit our podcasts homepage for transcripts and more episodes!

www.wandb.com/podcast

Get our podcast on these other platforms:

YouTube: http://wandb.me/youtube

Soundcloud: http://wandb.me/soundcloud

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.

https://wandb.ai/gallery

Peter Wang talks about his journey of being the CEO of and co-founding Anaconda, his perspective on the Python programming language, and its use for scientific computing.

Peter Wang has been developing commercial scientific computing and visualization software for over 15 years. He has extensive experience in software design and development across a broad range of areas, including 3D graphics, geophysics, large data simulation and visualization, financial risk modeling, and medical imaging.

Peter’s interests in the fundamentals of vector computing and interactive visualization led him to co-found Anaconda (formerly Continuum Analytics). Peter leads the open source and community innovation group.

As a creator of the PyData community and conferences, he devotes time and energy to growing the Python data science community and advocating and teaching Python at conferences around the world. Peter holds a BA in Physics from Cornell University.

Follow peter on Twitter: https://twitter.com/pwang​

https://www.anaconda.com/​

Intake: https://www.anaconda.com/blog/intake-...​

https://pydata.org/​

Scientific Data Management in the Coming Decade paper: https://arxiv.org/pdf/cs/0502008.pdf

Topics covered:

0:00​ (intro) Technology is not value neutral; Don't punt on ethics

1:30​ What is Conda?

2:57​ Peter's Story and Anaconda's beginning

6:45​ Do you ever regret choosing Python?

9:39​ On other programming languages

17:13​ Scientific Data Management in the Coming Decade

21:48​ Who are your customers?

26:24​ The ML hierarchy of needs

30:02​ The cybernetic era and Conway's Law

34:31​ R vs python

42:19​ Most underrated: Ethics - Don't Punt

46:50​ biggest bottlenecks: open-source, python

Visit our podcasts homepage for transcripts and more episodes!

www.wandb.com/podcast

Get our podcast on these other platforms:

YouTube: http://wandb.me/youtube

Soundcloud: http://wandb.me/soundcloud

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.

https://wandb.ai/gallery

Chris shares his journey starting from playing in R.E.M, becoming interested in physics to leading WIRED Magazine for 11 years. His robot fascination lead to starting a company that manufactures drones, and creating a community democratizing self-driving cars.

Chris Anderson is the CEO of 3D Robotics, founder of the Linux Foundation Dronecode Project and founder of the DIY Drones and DIY Robocars communities. From 2001 through 2012 he was the Editor in Chief of Wired Magazine. He's also the author of the New York Times bestsellers `The Long Tail` and `Free` and `Makers: The New Industrial Revolution`. In 2007 he was named to "Time 100," most influential men and women in the world.

Links discussed in this episode:

DIY Robocars: diyrobocars.com

Getting Started with Robocars: https://diyrobocars.com/2020/10/31/getting-started-with-robocars/

DIY Robotics Meet Up: https://www.meetup.com/DIYRobocars

Other Works

3DRobotics: https://www.3dr.com/

The Long Tail by Chris Anderson: https://www.amazon.com/Long-Tail-Future-Business-Selling/dp/1401309666/ref=sr_1_1?dchild=1&keywords=The+Long+Tail&qid=1610580178&s=books&sr=1-1

Interesting links Chris shared

OpenMV: https://openmv.io/

Intel Tracking Camera: https://www.intelrealsense.com/tracking-camera-t265/

Zumi Self-Driving Car Kit: https://www.robolink.com/zumi/

Possible Minds: Twenty-Five Ways of Looking at AI: https://www.amazon.com/Possible-Minds-Twenty-Five-Ways-Looking/dp/0525557997

Topics discussed:

0:00 sneak peek and intro

1:03 Battle of the REM's

3:35 A brief stint with Physics

5:09 Becoming a journalist and the woes of being a modern physicis

9:25 WIRED in the aughts

12:13 perspectives on "The Long Tail"

20:47 getting into drones

25:08 "Take a smartphone, add wings"

28:07 How did you get to autonomous racing cars?

33:30 COVID and virtual environments

38:40 Chris's hope for Robocars

40:54 Robocar hardware, software, sensors

53:49 path to Singularity/ regulations on drones

58:50 "the golden age of simulation"

1:00:22 biggest challenge in deploying ML models

Visit our podcasts homepage for transcripts and more episodes!

www.wandb.com/podcast

Get our podcast on these other platforms:

YouTube: http://wandb.me/youtube

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.

https://wandb.ai/gallery

Adrien shares his journey from making games that advance science (Eterna, Foldit) to creating a Streamlit, an open-source app framework enabling ML/Data practitioners to easily build powerful and interactive apps in a few hours.

Adrien is co-founder and CEO of Streamlit, an open-source app framework that helps create beautiful data apps in hours in pure Python. Dr. Treuille has been a Zoox VP, Google X project lead, and Computer Science faculty at Carnegie Mellon. He has won numerous scientific awards, including the MIT TR35. Adrien has been featured in the documentaries What Will the Future Be Like by PBS/NOVA, and Lo and Behold by Werner Herzog.

https://twitter.com/myelbows

https://www.linkedin.com/in/adrien-treuille-52215718/

https://www.streamlit.io/

https://eternagame.org/

https://fold.it/

Topics covered:

0:00 sneak peek/Streamlit

0:47 intro

1:21 from aspiring guitar player to machine learning

4:16 Foldit - games that train humans

10:08 Eterna - another game and its relation to ML

16:15 Research areas as a professor at Carnegie Mellon

18:07 the origin of Streamlit

23:53 evolution of Streamlit: data science-ing a pivot

30:20 on programming languages

32:20 what’s next for Streamlit

37:34 On meditation and work/life

41:40 Underrated aspect of Machine Learning

443:07 Biggest challenge in deploying ML in the real world

Visit our podcasts homepage for transcripts and more episodes!

www.wandb.com/podcast

Get our podcast on YouTube, Apple, Spotify, and Google!

YouTube: http://wandb.me/youtube

Apple Podcasts: http://wandb.me/apple-podcasts

Spotify: http://wandb.me/spotify

Google: http://wandb.me/google-podcasts

Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their work:

http://wandb.me/salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://wandb.me/slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.

We're thrilled to have Peter Norvig join us to talk about the evolution of deep learning, his industry-defining book, his work at Google, and what he thinks the future holds for machine learning research.

Peter Norvig is a Director of Research at Google Inc; previously he directed Google's core search algorithms group. He is co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and co-teacher of an Artificial Intelligence class that signed up 160,000. Prior to his work at Google, Norvig was NASA's chief computer scientist.

Peter's website:

https://norvig.com/

Topics covered:

0:00 singularity is in the eye of the beholder

0:32 introduction

1:09 project Euler

2:42 advent of code/pytudes

4:55 new sections in the new version of his book

10:32 unreasonable effectiveness of data Paper 15 years later

14:44 what advice would you give to a young researcher?

16:03 computing power in the evolution of deep learning

19:19 what's been surprising in the development of AI?

24:21 from alpha go to human-like intelligence

28:46 What in AI has been surprisingly hard or easy?

32:11 synthetic data and language

35:16 singularity is in the eye of the beholder

38:43 the future of python in ML and why he used it in his book

43:00 underrated topic in ML and bottlenecks in production

Visit our podcasts homepage for transcripts and more episodes!

https://www.wandb.com/podcast

Get our podcast on Apple, Spotify, and Google!

Apple Podcasts: https://bit.ly/2WdrUvI

Spotify: https://bit.ly/2SqtadF

Google: https://tiny.cc/GD_Google

We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!

Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:

https://tiny.cc/wb-salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

https://bit.ly/wb-slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.

https://wandb.ai/gallery

The story of Ray and what lead Robert to go from reinforcement learning researcher to creating open-source tools for machine learning and beyond

Robert is currently working on Ray, a high-performance distributed execution framework for AI applications. He studied mathematics at Harvard. He’s broadly interested in applied math, machine learning, and optimization, and was a member of the Statistical AI Lab, the AMPLab/RISELab, and the Berkeley AI Research Lab at UC Berkeley.

robertnishihara.com

https://anyscale.com/

https://github.com/ray-project/ray

https://twitter.com/robertnishihara

https://www.linkedin.com/in/robert-nishihara-b6465444/

Topics covered:

0:00 sneak peak + intro

1:09 what is Ray?

3:07 Spark and Ray

5:48 reinforcement learning

8:15 non-ml use cases of ray

10:00 RL in the real world and and common uses of Ray

13:49 Ppython in ML

16:38 from grad school to ML tools company

20:40 pulling product requirements in surprising directions

23:25 how to manage a large open source community

27:05 Ray Tune

29:35 where do you see bottlenecks in production?

31:39 An underrated aspect of Machine Learning

Visit our podcasts homepage for transcripts and more episodes!

www.wandb.com/podcast

Get our podcast on Apple, Spotify, and Google!

Apple Podcasts: https://bit.ly/2WdrUvI

Spotify: https://bit.ly/2SqtadF

Google: http://tiny.cc/GD_Google

Subscribe to our YouTube channel for videos of these podcasts and more Machine learning-related videos:

https://www.youtube.com/c/WeightsBiases

We started Weights and Biases to build tools for Machine Learning practitioners because we care a lot about the impact that Machine Learning can have in the world and we love working in the trenches with the people building these models. One of the most fun things about these building tools has been the conversations with these ML practitioners and learning about the interesting things they’re working on. This process has been so fun that we wanted to open it up to the world in the form of our new podcast called Gradient Dissent. We hope you have as much fun listening to it as we had making it!

Join our bi-weekly virtual salon and listen to industry leaders and researchers in machine learning share their research:

http://tiny.cc/wb-salon

Join our community of ML practitioners where we host AMA's, share interesting projects and meet other people working in Deep Learning:

http://bit.ly/wb-slack

Our gallery features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, and industry leaders sharing best practices.

https://app.wandb.ai/gallery